Overview

Dataset statistics

Number of variables31
Number of observations2195
Missing cells0
Missing cells (%)0.0%
Duplicate rows177
Duplicate rows (%)8.1%
Total size in memory494.3 KiB
Average record size in memory230.6 B

Variable types

Categorical15
Numeric16

Alerts

Dataset has 177 (8.1%) duplicate rowsDuplicates
Income is highly overall correlated with Wines and 12 other fieldsHigh correlation
Wines is highly overall correlated with Income and 10 other fieldsHigh correlation
Fruits is highly overall correlated with Income and 8 other fieldsHigh correlation
Meat is highly overall correlated with Income and 12 other fieldsHigh correlation
Fish is highly overall correlated with Income and 8 other fieldsHigh correlation
Sweet is highly overall correlated with Income and 8 other fieldsHigh correlation
Gold is highly overall correlated with Income and 10 other fieldsHigh correlation
NumDealsPurchases is highly overall correlated with HasChildrenHigh correlation
NumWebPurchases is highly overall correlated with Income and 7 other fieldsHigh correlation
NumCatalogPurchases is highly overall correlated with Income and 11 other fieldsHigh correlation
NumStorePurchases is highly overall correlated with Income and 10 other fieldsHigh correlation
NumWebVisitsMonth is highly overall correlated with Income and 3 other fieldsHigh correlation
TotalSpent is highly overall correlated with Income and 11 other fieldsHigh correlation
Age is highly overall correlated with AgeGroup and 1 other fieldsHigh correlation
Education is highly overall correlated with EducationLevel and 1 other fieldsHigh correlation
Kidhome is highly overall correlated with Children and 2 other fieldsHigh correlation
Teenhome is highly overall correlated with Children and 1 other fieldsHigh correlation
Children is highly overall correlated with Kidhome and 3 other fieldsHigh correlation
FamilySize is highly overall correlated with Kidhome and 3 other fieldsHigh correlation
TotalAcceptedCmp is highly overall correlated with AcceptedSmthHigh correlation
AcceptedSmth is highly overall correlated with TotalAcceptedCmpHigh correlation
HasChildren is highly overall correlated with Income and 8 other fieldsHigh correlation
BoughtCatalog is highly overall correlated with Income and 7 other fieldsHigh correlation
AgeGroup is highly overall correlated with Age and 1 other fieldsHigh correlation
InRelationship is highly overall correlated with FamilySizeHigh correlation
EducationLevel is highly overall correlated with Education and 1 other fieldsHigh correlation
MedEducationTotalSpent is highly overall correlated with Education and 1 other fieldsHigh correlation
MedAgeGroupTotalSpent is highly overall correlated with Age and 1 other fieldsHigh correlation
TotalAcceptedCmp is highly imbalanced (57.0%)Imbalance
Recency has 27 (1.2%) zerosZeros
Fruits has 391 (17.8%) zerosZeros
Fish has 379 (17.3%) zerosZeros
Sweet has 411 (18.7%) zerosZeros
Gold has 61 (2.8%) zerosZeros
NumDealsPurchases has 34 (1.5%) zerosZeros
NumWebPurchases has 37 (1.7%) zerosZeros
NumCatalogPurchases has 564 (25.7%) zerosZeros

Reproduction

Analysis started2023-11-05 16:50:29.100407
Analysis finished2023-11-05 16:51:33.013032
Duration1 minute and 3.91 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Education
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size84.0 KiB
Graduation
1109 
PhD
471 
Master
363 
2n Cycle
198 
Basic
 
54

Length

Max length10
Median length10
Mean length7.5330296
Min length3

Characters and Unicode

Total characters16535
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowGraduation
3rd rowGraduation
4th rowGraduation
5th rowPhD

Common Values

ValueCountFrequency (%)
Graduation 1109
50.5%
PhD 471
21.5%
Master 363
 
16.5%
2n Cycle 198
 
9.0%
Basic 54
 
2.5%

Length

2023-11-05T19:51:33.262792image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T19:51:33.699439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
graduation 1109
46.3%
phd 471
19.7%
master 363
 
15.2%
2n 198
 
8.3%
cycle 198
 
8.3%
basic 54
 
2.3%

Most occurring characters

ValueCountFrequency (%)
a 2635
15.9%
r 1472
8.9%
t 1472
8.9%
n 1307
 
7.9%
i 1163
 
7.0%
G 1109
 
6.7%
d 1109
 
6.7%
u 1109
 
6.7%
o 1109
 
6.7%
e 561
 
3.4%
Other values (12) 3489
21.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13473
81.5%
Uppercase Letter 2666
 
16.1%
Decimal Number 198
 
1.2%
Space Separator 198
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2635
19.6%
r 1472
10.9%
t 1472
10.9%
n 1307
9.7%
i 1163
8.6%
d 1109
8.2%
u 1109
8.2%
o 1109
8.2%
e 561
 
4.2%
h 471
 
3.5%
Other values (4) 1065
7.9%
Uppercase Letter
ValueCountFrequency (%)
G 1109
41.6%
D 471
17.7%
P 471
17.7%
M 363
 
13.6%
C 198
 
7.4%
B 54
 
2.0%
Decimal Number
ValueCountFrequency (%)
2 198
100.0%
Space Separator
ValueCountFrequency (%)
198
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16139
97.6%
Common 396
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2635
16.3%
r 1472
9.1%
t 1472
9.1%
n 1307
8.1%
i 1163
 
7.2%
G 1109
 
6.9%
d 1109
 
6.9%
u 1109
 
6.9%
o 1109
 
6.9%
e 561
 
3.5%
Other values (10) 3093
19.2%
Common
ValueCountFrequency (%)
2 198
50.0%
198
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16535
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2635
15.9%
r 1472
8.9%
t 1472
8.9%
n 1307
 
7.9%
i 1163
 
7.0%
G 1109
 
6.7%
d 1109
 
6.7%
u 1109
 
6.7%
o 1109
 
6.7%
e 561
 
3.4%
Other values (12) 3489
21.1%

Income
Real number (ℝ)

HIGH CORRELATION 

Distinct1953
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51784.119
Minimum3502
Maximum105471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2023-11-05T19:51:34.102645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3502
5-th percentile19502.5
Q135428.5
median51373
Q368334
95-th percentile83829
Maximum105471
Range101969
Interquartile range (IQR)32905.5

Descriptive statistics

Standard deviation20502.347
Coefficient of variation (CV)0.39591958
Kurtosis-0.89630948
Mean51784.119
Median Absolute Deviation (MAD)16438
Skewness0.025150338
Sum1.1366614 × 108
Variance4.2034623 × 108
MonotonicityNot monotonic
2023-11-05T19:51:34.448259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7500 12
 
0.5%
35860 4
 
0.2%
48432 3
 
0.1%
83844 3
 
0.1%
37760 3
 
0.1%
46098 3
 
0.1%
18929 3
 
0.1%
63841 3
 
0.1%
80134 3
 
0.1%
18690 3
 
0.1%
Other values (1943) 2155
98.2%
ValueCountFrequency (%)
3502 1
 
< 0.1%
4861 1
 
< 0.1%
5305 1
 
< 0.1%
7500 12
0.5%
8820 1
 
< 0.1%
8940 1
 
< 0.1%
9255 1
 
< 0.1%
9548 1
 
< 0.1%
9722 1
 
< 0.1%
10245 1
 
< 0.1%
ValueCountFrequency (%)
105471 1
< 0.1%
102692 1
< 0.1%
102160 1
< 0.1%
101970 1
< 0.1%
98777 2
0.1%
96876 1
< 0.1%
96843 1
< 0.1%
96547 1
< 0.1%
95529 1
< 0.1%
95169 1
< 0.1%

Kidhome
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size98.8 KiB
0
1269 
1
880 
2
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2195
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1269
57.8%
1 880
40.1%
2 46
 
2.1%

Length

2023-11-05T19:51:34.779966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T19:51:35.082819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1269
57.8%
1 880
40.1%
2 46
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1269
57.8%
1 880
40.1%
2 46
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2195
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1269
57.8%
1 880
40.1%
2 46
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2195
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1269
57.8%
1 880
40.1%
2 46
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1269
57.8%
1 880
40.1%
2 46
 
2.1%

Teenhome
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size98.8 KiB
0
1134 
1
1011 
2
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2195
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1134
51.7%
1 1011
46.1%
2 50
 
2.3%

Length

2023-11-05T19:51:35.450243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T19:51:35.710782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1134
51.7%
1 1011
46.1%
2 50
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1134
51.7%
1 1011
46.1%
2 50
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2195
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1134
51.7%
1 1011
46.1%
2 50
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2195
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1134
51.7%
1 1011
46.1%
2 50
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1134
51.7%
1 1011
46.1%
2 50
 
2.3%

Recency
Real number (ℝ)

ZEROS 

Distinct100
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.029613
Minimum0
Maximum99
Zeros27
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2023-11-05T19:51:36.039145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.91908
Coefficient of variation (CV)0.58982884
Kurtosis-1.1984055
Mean49.029613
Median Absolute Deviation (MAD)25
Skewness-0.00058987382
Sum107620
Variance836.31316
MonotonicityNot monotonic
2023-11-05T19:51:36.417413image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 37
 
1.7%
54 32
 
1.5%
30 32
 
1.5%
46 31
 
1.4%
3 29
 
1.3%
92 29
 
1.3%
71 29
 
1.3%
49 29
 
1.3%
65 29
 
1.3%
24 28
 
1.3%
Other values (90) 1890
86.1%
ValueCountFrequency (%)
0 27
1.2%
1 24
1.1%
2 27
1.2%
3 29
1.3%
4 26
1.2%
5 15
0.7%
6 21
1.0%
7 12
0.5%
8 25
1.1%
9 23
1.0%
ValueCountFrequency (%)
99 16
0.7%
98 21
1.0%
97 20
0.9%
96 23
1.0%
95 18
0.8%
94 26
1.2%
93 21
1.0%
92 29
1.3%
91 18
0.8%
90 20
0.9%

Wines
Real number (ℝ)

HIGH CORRELATION 

Distinct774
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.38405
Minimum0
Maximum1493
Zeros13
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2023-11-05T19:51:36.866891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q124
median179
Q3508
95-th percentile1001
Maximum1493
Range1493
Interquartile range (IQR)484

Descriptive statistics

Standard deviation337.76734
Coefficient of variation (CV)1.0988447
Kurtosis0.5607358
Mean307.38405
Median Absolute Deviation (MAD)169
Skewness1.1609551
Sum674708
Variance114086.78
MonotonicityNot monotonic
2023-11-05T19:51:37.569395image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 41
 
1.9%
6 36
 
1.6%
5 36
 
1.6%
4 33
 
1.5%
1 33
 
1.5%
3 30
 
1.4%
8 29
 
1.3%
9 27
 
1.2%
12 25
 
1.1%
14 24
 
1.1%
Other values (764) 1881
85.7%
ValueCountFrequency (%)
0 13
 
0.6%
1 33
1.5%
2 41
1.9%
3 30
1.4%
4 33
1.5%
5 36
1.6%
6 36
1.6%
7 21
1.0%
8 29
1.3%
9 27
1.2%
ValueCountFrequency (%)
1493 1
< 0.1%
1492 2
0.1%
1486 1
< 0.1%
1478 2
0.1%
1462 1
< 0.1%
1459 1
< 0.1%
1449 1
< 0.1%
1396 1
< 0.1%
1394 1
< 0.1%
1379 1
< 0.1%

Fruits
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct158
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.503872
Minimum0
Maximum199
Zeros391
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2023-11-05T19:51:38.086838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q333
95-th percentile122.3
Maximum199
Range199
Interquartile range (IQR)31

Descriptive statistics

Standard deviation39.845261
Coefficient of variation (CV)1.5033751
Kurtosis4.0180721
Mean26.503872
Median Absolute Deviation (MAD)8
Skewness2.0925381
Sum58176
Variance1587.6448
MonotonicityNot monotonic
2023-11-05T19:51:38.509669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 391
 
17.8%
1 152
 
6.9%
2 116
 
5.3%
3 114
 
5.2%
4 101
 
4.6%
7 67
 
3.1%
5 62
 
2.8%
6 61
 
2.8%
12 50
 
2.3%
8 48
 
2.2%
Other values (148) 1033
47.1%
ValueCountFrequency (%)
0 391
17.8%
1 152
 
6.9%
2 116
 
5.3%
3 114
 
5.2%
4 101
 
4.6%
5 62
 
2.8%
6 61
 
2.8%
7 67
 
3.1%
8 48
 
2.2%
9 35
 
1.6%
ValueCountFrequency (%)
199 2
0.1%
197 1
 
< 0.1%
194 3
0.1%
193 2
0.1%
190 1
 
< 0.1%
189 1
 
< 0.1%
185 2
0.1%
184 1
 
< 0.1%
183 3
0.1%
181 1
 
< 0.1%

Meat
Real number (ℝ)

HIGH CORRELATION 

Distinct550
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.20547
Minimum0
Maximum984
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2023-11-05T19:51:38.959472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q116
median68
Q3232.5
95-th percentile680.1
Maximum984
Range984
Interquartile range (IQR)216.5

Descriptive statistics

Standard deviation215.51534
Coefficient of variation (CV)1.3045291
Kurtosis2.2941498
Mean165.20547
Median Absolute Deviation (MAD)60
Skewness1.7154855
Sum362626
Variance46446.862
MonotonicityNot monotonic
2023-11-05T19:51:39.405826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 53
 
2.4%
11 49
 
2.2%
5 49
 
2.2%
8 44
 
2.0%
6 41
 
1.9%
10 40
 
1.8%
3 37
 
1.7%
9 36
 
1.6%
16 34
 
1.5%
12 32
 
1.5%
Other values (540) 1780
81.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 12
 
0.5%
2 29
1.3%
3 37
1.7%
4 30
1.4%
5 49
2.2%
6 41
1.9%
7 53
2.4%
8 44
2.0%
9 36
1.6%
ValueCountFrequency (%)
984 1
< 0.1%
981 1
< 0.1%
974 1
< 0.1%
968 1
< 0.1%
961 1
< 0.1%
951 2
0.1%
946 1
< 0.1%
940 1
< 0.1%
936 1
< 0.1%
935 1
< 0.1%

Fish
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct181
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.917084
Minimum0
Maximum259
Zeros379
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2023-11-05T19:51:39.709491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q350
95-th percentile169.6
Maximum259
Range259
Interquartile range (IQR)47

Descriptive statistics

Standard deviation54.897455
Coefficient of variation (CV)1.447829
Kurtosis3.0285932
Mean37.917084
Median Absolute Deviation (MAD)12
Skewness1.9055516
Sum83228
Variance3013.7306
MonotonicityNot monotonic
2023-11-05T19:51:40.002264image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 379
 
17.3%
2 147
 
6.7%
3 128
 
5.8%
4 107
 
4.9%
6 81
 
3.7%
7 63
 
2.9%
8 57
 
2.6%
10 54
 
2.5%
13 48
 
2.2%
11 46
 
2.1%
Other values (171) 1085
49.4%
ValueCountFrequency (%)
0 379
17.3%
1 2
 
0.1%
2 147
 
6.7%
3 128
 
5.8%
4 107
 
4.9%
6 81
 
3.7%
7 63
 
2.9%
8 57
 
2.6%
10 54
 
2.5%
11 46
 
2.1%
ValueCountFrequency (%)
259 1
 
< 0.1%
258 3
0.1%
254 1
 
< 0.1%
253 1
 
< 0.1%
250 3
0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
242 1
 
< 0.1%
240 2
0.1%
237 2
0.1%

Sweet
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct175
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.120273
Minimum0
Maximum198
Zeros411
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2023-11-05T19:51:40.291617image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q334
95-th percentile125.3
Maximum198
Range198
Interquartile range (IQR)33

Descriptive statistics

Standard deviation40.888248
Coefficient of variation (CV)1.5076635
Kurtosis3.7858018
Mean27.120273
Median Absolute Deviation (MAD)8
Skewness2.0606236
Sum59529
Variance1671.8488
MonotonicityNot monotonic
2023-11-05T19:51:40.578509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 411
 
18.7%
1 152
 
6.9%
2 120
 
5.5%
3 99
 
4.5%
4 78
 
3.6%
5 65
 
3.0%
6 63
 
2.9%
7 57
 
2.6%
8 56
 
2.6%
12 44
 
2.0%
Other values (165) 1050
47.8%
ValueCountFrequency (%)
0 411
18.7%
1 152
 
6.9%
2 120
 
5.5%
3 99
 
4.5%
4 78
 
3.6%
5 65
 
3.0%
6 63
 
2.9%
7 57
 
2.6%
8 56
 
2.6%
9 42
 
1.9%
ValueCountFrequency (%)
198 1
 
< 0.1%
197 1
 
< 0.1%
196 1
 
< 0.1%
195 1
 
< 0.1%
194 3
0.1%
192 3
0.1%
191 1
 
< 0.1%
189 2
0.1%
188 1
 
< 0.1%
187 1
 
< 0.1%

Gold
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct209
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.844647
Minimum0
Maximum249
Zeros61
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2023-11-05T19:51:40.837808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median25
Q356
95-th percentile163
Maximum249
Range249
Interquartile range (IQR)47

Descriptive statistics

Standard deviation50.984027
Coefficient of variation (CV)1.1628336
Kurtosis2.7707724
Mean43.844647
Median Absolute Deviation (MAD)19
Skewness1.7790681
Sum96239
Variance2599.371
MonotonicityNot monotonic
2023-11-05T19:51:41.088840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 68
 
3.1%
3 67
 
3.1%
1 64
 
2.9%
5 63
 
2.9%
12 61
 
2.8%
0 61
 
2.8%
2 59
 
2.7%
6 55
 
2.5%
7 52
 
2.4%
10 49
 
2.2%
Other values (199) 1596
72.7%
ValueCountFrequency (%)
0 61
2.8%
1 64
2.9%
2 59
2.7%
3 67
3.1%
4 68
3.1%
5 63
2.9%
6 55
2.5%
7 52
2.4%
8 39
1.8%
9 43
2.0%
ValueCountFrequency (%)
249 1
 
< 0.1%
248 1
 
< 0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
245 1
 
< 0.1%
242 2
 
0.1%
241 6
0.3%
233 1
 
< 0.1%
232 2
 
0.1%
231 1
 
< 0.1%

NumDealsPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.301139
Minimum0
Maximum15
Zeros34
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2023-11-05T19:51:41.374830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8075032
Coefficient of variation (CV)0.78548201
Kurtosis5.9658652
Mean2.301139
Median Absolute Deviation (MAD)1
Skewness2.0427098
Sum5051
Variance3.267068
MonotonicityNot monotonic
2023-11-05T19:51:41.564907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 956
43.6%
2 493
22.5%
3 293
 
13.3%
4 187
 
8.5%
5 94
 
4.3%
6 60
 
2.7%
7 39
 
1.8%
0 34
 
1.5%
8 14
 
0.6%
9 8
 
0.4%
Other values (5) 17
 
0.8%
ValueCountFrequency (%)
0 34
 
1.5%
1 956
43.6%
2 493
22.5%
3 293
 
13.3%
4 187
 
8.5%
5 94
 
4.3%
6 60
 
2.7%
7 39
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
ValueCountFrequency (%)
15 1
 
< 0.1%
13 3
 
0.1%
12 3
 
0.1%
11 5
 
0.2%
10 5
 
0.2%
9 8
 
0.4%
8 14
 
0.6%
7 39
1.8%
6 60
2.7%
5 94
4.3%

NumWebPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0842825
Minimum0
Maximum11
Zeros37
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2023-11-05T19:51:41.764578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6228614
Coefficient of variation (CV)0.64218413
Kurtosis-0.26257254
Mean4.0842825
Median Absolute Deviation (MAD)2
Skewness0.69593105
Sum8965
Variance6.8794019
MonotonicityNot monotonic
2023-11-05T19:51:42.045451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 367
16.7%
1 344
15.7%
3 333
15.2%
4 276
12.6%
5 219
10.0%
6 201
9.2%
7 154
7.0%
8 102
 
4.6%
9 75
 
3.4%
11 44
 
2.0%
Other values (2) 80
 
3.6%
ValueCountFrequency (%)
0 37
 
1.7%
1 344
15.7%
2 367
16.7%
3 333
15.2%
4 276
12.6%
5 219
10.0%
6 201
9.2%
7 154
7.0%
8 102
 
4.6%
9 75
 
3.4%
ValueCountFrequency (%)
11 44
 
2.0%
10 43
 
2.0%
9 75
 
3.4%
8 102
 
4.6%
7 154
7.0%
6 201
9.2%
5 219
10.0%
4 276
12.6%
3 333
15.2%
2 367
16.7%

NumCatalogPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6437358
Minimum0
Maximum11
Zeros564
Zeros (%)25.7%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2023-11-05T19:51:42.242108image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile8
Maximum11
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7477845
Coefficient of variation (CV)1.0393567
Kurtosis0.400522
Mean2.6437358
Median Absolute Deviation (MAD)2
Skewness1.0985023
Sum5803
Variance7.5503197
MonotonicityNot monotonic
2023-11-05T19:51:42.460315image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 564
25.7%
1 488
22.2%
2 274
12.5%
3 182
 
8.3%
4 181
 
8.2%
5 137
 
6.2%
6 127
 
5.8%
7 79
 
3.6%
8 55
 
2.5%
10 47
 
2.1%
Other values (2) 61
 
2.8%
ValueCountFrequency (%)
0 564
25.7%
1 488
22.2%
2 274
12.5%
3 182
 
8.3%
4 181
 
8.2%
5 137
 
6.2%
6 127
 
5.8%
7 79
 
3.6%
8 55
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
11 19
 
0.9%
10 47
 
2.1%
9 42
 
1.9%
8 55
 
2.5%
7 79
 
3.6%
6 127
5.8%
5 137
6.2%
4 181
8.2%
3 182
8.3%
2 274
12.5%

NumStorePurchases
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8487472
Minimum0
Maximum13
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2023-11-05T19:51:42.645148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2274591
Coefficient of variation (CV)0.55182059
Kurtosis-0.64547572
Mean5.8487472
Median Absolute Deviation (MAD)2
Skewness0.71960829
Sum12838
Variance10.416492
MonotonicityNot monotonic
2023-11-05T19:51:42.838634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 483
22.0%
4 318
14.5%
2 218
9.9%
5 211
9.6%
6 177
 
8.1%
8 147
 
6.7%
7 141
 
6.4%
10 124
 
5.6%
9 106
 
4.8%
12 104
 
4.7%
Other values (3) 166
 
7.6%
ValueCountFrequency (%)
0 3
 
0.1%
2 218
9.9%
3 483
22.0%
4 318
14.5%
5 211
9.6%
6 177
 
8.1%
7 141
 
6.4%
8 147
 
6.7%
9 106
 
4.8%
10 124
 
5.6%
ValueCountFrequency (%)
13 83
 
3.8%
12 104
 
4.7%
11 80
 
3.6%
10 124
 
5.6%
9 106
 
4.8%
8 147
6.7%
7 141
6.4%
6 177
8.1%
5 211
9.6%
4 318
14.5%

NumWebVisitsMonth
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3075171
Minimum0
Maximum14
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2023-11-05T19:51:43.044305image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum14
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2991245
Coefficient of variation (CV)0.43318269
Kurtosis-0.77331129
Mean5.3075171
Median Absolute Deviation (MAD)2
Skewness-0.28544135
Sum11650
Variance5.2859735
MonotonicityNot monotonic
2023-11-05T19:51:43.340729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
7 387
17.6%
8 340
15.5%
6 334
15.2%
5 278
12.7%
4 216
9.8%
3 203
9.2%
2 201
9.2%
1 143
 
6.5%
9 82
 
3.7%
0 5
 
0.2%
Other values (3) 6
 
0.3%
ValueCountFrequency (%)
0 5
 
0.2%
1 143
 
6.5%
2 201
9.2%
3 203
9.2%
4 216
9.8%
5 278
12.7%
6 334
15.2%
7 387
17.6%
8 340
15.5%
9 82
 
3.7%
ValueCountFrequency (%)
14 2
 
0.1%
13 1
 
< 0.1%
10 3
 
0.1%
9 82
 
3.7%
8 340
15.5%
7 387
17.6%
6 334
15.2%
5 278
12.7%
4 216
9.8%
3 203
9.2%

Response
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size84.0 KiB
0
1862 
1
333 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2195
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1862
84.8%
1 333
 
15.2%

Length

2023-11-05T19:51:43.560225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T19:51:43.746247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1862
84.8%
1 333
 
15.2%

Most occurring characters

ValueCountFrequency (%)
0 1862
84.8%
1 333
 
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2195
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1862
84.8%
1 333
 
15.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2195
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1862
84.8%
1 333
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1862
84.8%
1 333
 
15.2%

TotalSpent
Real number (ℝ)

HIGH CORRELATION 

Distinct1042
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean607.9754
Minimum5
Maximum2525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2023-11-05T19:51:43.951974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile22
Q169
median400
Q31048
95-th percentile1778.3
Maximum2525
Range2520
Interquartile range (IQR)979

Descriptive statistics

Standard deviation601.92621
Coefficient of variation (CV)0.99005028
Kurtosis-0.34107337
Mean607.9754
Median Absolute Deviation (MAD)357
Skewness0.85546372
Sum1334506
Variance362315.17
MonotonicityNot monotonic
2023-11-05T19:51:44.254216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 18
 
0.8%
22 17
 
0.8%
57 16
 
0.7%
44 15
 
0.7%
55 15
 
0.7%
37 14
 
0.6%
38 14
 
0.6%
20 14
 
0.6%
43 14
 
0.6%
48 14
 
0.6%
Other values (1032) 2044
93.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
6 1
 
< 0.1%
8 2
 
0.1%
9 1
 
< 0.1%
10 5
0.2%
11 5
0.2%
12 2
 
0.1%
13 6
0.3%
14 3
 
0.1%
15 10
0.5%
ValueCountFrequency (%)
2525 2
0.1%
2524 1
< 0.1%
2486 1
< 0.1%
2440 1
< 0.1%
2352 1
< 0.1%
2349 1
< 0.1%
2346 1
< 0.1%
2302 2
0.1%
2283 1
< 0.1%
2279 1
< 0.1%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.088383
Minimum18
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2023-11-05T19:51:44.528805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26
Q137
median44
Q355
95-th percentile64
Maximum74
Range56
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.695874
Coefficient of variation (CV)0.25939883
Kurtosis-0.79761736
Mean45.088383
Median Absolute Deviation (MAD)9
Skewness0.090102309
Sum98969
Variance136.79346
MonotonicityNot monotonic
2023-11-05T19:51:44.776748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 88
 
4.0%
43 84
 
3.8%
39 82
 
3.7%
42 78
 
3.6%
36 76
 
3.5%
44 75
 
3.4%
49 74
 
3.4%
41 71
 
3.2%
40 69
 
3.1%
45 69
 
3.1%
Other values (46) 1429
65.1%
ValueCountFrequency (%)
18 2
 
0.1%
19 5
 
0.2%
20 3
 
0.1%
21 5
 
0.2%
22 13
0.6%
23 14
0.6%
24 18
0.8%
25 29
1.3%
26 29
1.3%
27 27
1.2%
ValueCountFrequency (%)
74 1
 
< 0.1%
73 1
 
< 0.1%
71 6
 
0.3%
70 7
 
0.3%
69 7
 
0.3%
68 16
0.7%
67 16
0.7%
66 21
1.0%
65 29
1.3%
64 28
1.3%

Children
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size98.8 KiB
1
1109 
0
624 
2
412 
3
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2195
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1109
50.5%
0 624
28.4%
2 412
 
18.8%
3 50
 
2.3%

Length

2023-11-05T19:51:45.040153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T19:51:45.340382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1109
50.5%
0 624
28.4%
2 412
 
18.8%
3 50
 
2.3%

Most occurring characters

ValueCountFrequency (%)
1 1109
50.5%
0 624
28.4%
2 412
 
18.8%
3 50
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2195
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1109
50.5%
0 624
28.4%
2 412
 
18.8%
3 50
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2195
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1109
50.5%
0 624
28.4%
2 412
 
18.8%
3 50
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1109
50.5%
0 624
28.4%
2 412
 
18.8%
3 50
 
2.3%

FamilySize
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size98.8 KiB
3
873 
2
751 
4
293 
1
247 
5
 
31

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2195
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 873
39.8%
2 751
34.2%
4 293
 
13.3%
1 247
 
11.3%
5 31
 
1.4%

Length

2023-11-05T19:51:45.610934image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T19:51:46.315049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
3 873
39.8%
2 751
34.2%
4 293
 
13.3%
1 247
 
11.3%
5 31
 
1.4%

Most occurring characters

ValueCountFrequency (%)
3 873
39.8%
2 751
34.2%
4 293
 
13.3%
1 247
 
11.3%
5 31
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2195
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 873
39.8%
2 751
34.2%
4 293
 
13.3%
1 247
 
11.3%
5 31
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2195
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 873
39.8%
2 751
34.2%
4 293
 
13.3%
1 247
 
11.3%
5 31
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 873
39.8%
2 751
34.2%
4 293
 
13.3%
1 247
 
11.3%
5 31
 
1.4%

TotalAcceptedCmp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size98.8 KiB
0
1738 
1
321 
2
 
81
3
 
44
4
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2195
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1738
79.2%
1 321
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Length

2023-11-05T19:51:46.566595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T19:51:46.773540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1738
79.2%
1 321
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1738
79.2%
1 321
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2195
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1738
79.2%
1 321
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2195
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1738
79.2%
1 321
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1738
79.2%
1 321
 
14.6%
2 81
 
3.7%
3 44
 
2.0%
4 11
 
0.5%

AcceptedSmth
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size84.0 KiB
0
1738 
1
457 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2195
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1738
79.2%
1 457
 
20.8%

Length

2023-11-05T19:51:47.042405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T19:51:47.232616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1738
79.2%
1 457
 
20.8%

Most occurring characters

ValueCountFrequency (%)
0 1738
79.2%
1 457
 
20.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2195
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1738
79.2%
1 457
 
20.8%

Most occurring scripts

ValueCountFrequency (%)
Common 2195
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1738
79.2%
1 457
 
20.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1738
79.2%
1 457
 
20.8%

HasChildren
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size84.0 KiB
1
1571 
0
624 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2195
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1571
71.6%
0 624
 
28.4%

Length

2023-11-05T19:51:47.456110image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T19:51:47.726694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1571
71.6%
0 624
 
28.4%

Most occurring characters

ValueCountFrequency (%)
1 1571
71.6%
0 624
 
28.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2195
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1571
71.6%
0 624
 
28.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2195
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1571
71.6%
0 624
 
28.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1571
71.6%
0 624
 
28.4%

BoughtCatalog
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size84.0 KiB
1
1631 
0
564 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2195
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 1631
74.3%
0 564
 
25.7%

Length

2023-11-05T19:51:47.992827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T19:51:48.240202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1631
74.3%
0 564
 
25.7%

Most occurring characters

ValueCountFrequency (%)
1 1631
74.3%
0 564
 
25.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2195
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1631
74.3%
0 564
 
25.7%

Most occurring scripts

ValueCountFrequency (%)
Common 2195
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1631
74.3%
0 564
 
25.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1631
74.3%
0 564
 
25.7%

AgeGroup
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size84.0 KiB
Youth
1141 
Average
744 
Elderly
310 

Length

Max length7
Median length5
Mean length5.9603645
Min length5

Characters and Unicode

Total characters13083
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAverage
2nd rowElderly
3rd rowAverage
4th rowYouth
5th rowYouth

Common Values

ValueCountFrequency (%)
Youth 1141
52.0%
Average 744
33.9%
Elderly 310
 
14.1%

Length

2023-11-05T19:51:48.514875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T19:51:48.823382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
youth 1141
52.0%
average 744
33.9%
elderly 310
 
14.1%

Most occurring characters

ValueCountFrequency (%)
e 1798
13.7%
Y 1141
8.7%
o 1141
8.7%
u 1141
8.7%
t 1141
8.7%
h 1141
8.7%
r 1054
8.1%
A 744
 
5.7%
v 744
 
5.7%
a 744
 
5.7%
Other values (5) 2294
17.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10888
83.2%
Uppercase Letter 2195
 
16.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1798
16.5%
o 1141
10.5%
u 1141
10.5%
t 1141
10.5%
h 1141
10.5%
r 1054
9.7%
v 744
6.8%
a 744
6.8%
g 744
6.8%
l 620
 
5.7%
Other values (2) 620
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
Y 1141
52.0%
A 744
33.9%
E 310
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 13083
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1798
13.7%
Y 1141
8.7%
o 1141
8.7%
u 1141
8.7%
t 1141
8.7%
h 1141
8.7%
r 1054
8.1%
A 744
 
5.7%
v 744
 
5.7%
a 744
 
5.7%
Other values (5) 2294
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13083
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1798
13.7%
Y 1141
8.7%
o 1141
8.7%
u 1141
8.7%
t 1141
8.7%
h 1141
8.7%
r 1054
8.1%
A 744
 
5.7%
v 744
 
5.7%
a 744
 
5.7%
Other values (5) 2294
17.5%

CustomerLastDays
Real number (ℝ)

Distinct660
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean354.13895
Minimum0
Maximum699
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2023-11-05T19:51:49.128194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38
Q1180.5
median356
Q3529
95-th percentile667
Maximum699
Range699
Interquartile range (IQR)348.5

Descriptive statistics

Standard deviation202.34602
Coefficient of variation (CV)0.57137464
Kurtosis-1.2004498
Mean354.13895
Median Absolute Deviation (MAD)174
Skewness-0.020819167
Sum777335
Variance40943.911
MonotonicityNot monotonic
2023-11-05T19:51:49.444156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
667 12
 
0.5%
500 11
 
0.5%
655 11
 
0.5%
48 11
 
0.5%
38 10
 
0.5%
313 10
 
0.5%
98 9
 
0.4%
608 9
 
0.4%
543 9
 
0.4%
85 9
 
0.4%
Other values (650) 2094
95.4%
ValueCountFrequency (%)
0 2
 
0.1%
1 3
0.1%
2 3
0.1%
3 4
0.2%
4 5
0.2%
5 2
 
0.1%
6 1
 
< 0.1%
7 5
0.2%
8 2
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
699 1
 
< 0.1%
698 1
 
< 0.1%
697 4
0.2%
696 3
0.1%
695 5
0.2%
694 3
0.1%
693 1
 
< 0.1%
692 3
0.1%
691 4
0.2%
690 7
0.3%

InRelationship
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size84.0 KiB
1
1420 
0
775 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2195
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1420
64.7%
0 775
35.3%

Length

2023-11-05T19:51:49.793020image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T19:51:50.067153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1420
64.7%
0 775
35.3%

Most occurring characters

ValueCountFrequency (%)
1 1420
64.7%
0 775
35.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2195
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1420
64.7%
0 775
35.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2195
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1420
64.7%
0 775
35.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1420
64.7%
0 775
35.3%

EducationLevel
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size84.0 KiB
Graduate
1109 
Postgraduate
834 
Undergraduate
252 

Length

Max length13
Median length8
Mean length10.09385
Min length8

Characters and Unicode

Total characters22156
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduate
2nd rowGraduate
3rd rowGraduate
4th rowGraduate
5th rowPostgraduate

Common Values

ValueCountFrequency (%)
Graduate 1109
50.5%
Postgraduate 834
38.0%
Undergraduate 252
 
11.5%

Length

2023-11-05T19:51:50.273038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T19:51:50.486172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
graduate 1109
50.5%
postgraduate 834
38.0%
undergraduate 252
 
11.5%

Most occurring characters

ValueCountFrequency (%)
a 4390
19.8%
t 3029
13.7%
r 2447
11.0%
d 2447
11.0%
e 2447
11.0%
u 2195
9.9%
G 1109
 
5.0%
g 1086
 
4.9%
P 834
 
3.8%
o 834
 
3.8%
Other values (3) 1338
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19961
90.1%
Uppercase Letter 2195
 
9.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4390
22.0%
t 3029
15.2%
r 2447
12.3%
d 2447
12.3%
e 2447
12.3%
u 2195
11.0%
g 1086
 
5.4%
o 834
 
4.2%
s 834
 
4.2%
n 252
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
G 1109
50.5%
P 834
38.0%
U 252
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 22156
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4390
19.8%
t 3029
13.7%
r 2447
11.0%
d 2447
11.0%
e 2447
11.0%
u 2195
9.9%
G 1109
 
5.0%
g 1086
 
4.9%
P 834
 
3.8%
o 834
 
3.8%
Other values (3) 1338
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4390
19.8%
t 3029
13.7%
r 2447
11.0%
d 2447
11.0%
e 2447
11.0%
u 2195
9.9%
G 1109
 
5.0%
g 1086
 
4.9%
P 834
 
3.8%
o 834
 
3.8%
Other values (3) 1338
 
6.0%

MedEducationTotalSpent
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size98.8 KiB
415.0
1109 
496.0
471 
390.0
363 
207.5
198 
57.0
 
54

Length

Max length5
Median length5
Mean length4.9753986
Min length4

Characters and Unicode

Total characters10921
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row415.0
2nd row415.0
3rd row415.0
4th row415.0
5th row496.0

Common Values

ValueCountFrequency (%)
415.0 1109
50.5%
496.0 471
21.5%
390.0 363
 
16.5%
207.5 198
 
9.0%
57.0 54
 
2.5%

Length

2023-11-05T19:51:50.722080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T19:51:50.937853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
415.0 1109
50.5%
496.0 471
21.5%
390.0 363
 
16.5%
207.5 198
 
9.0%
57.0 54
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 2558
23.4%
. 2195
20.1%
4 1580
14.5%
5 1361
12.5%
1 1109
10.2%
9 834
 
7.6%
6 471
 
4.3%
3 363
 
3.3%
7 252
 
2.3%
2 198
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8726
79.9%
Other Punctuation 2195
 
20.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2558
29.3%
4 1580
18.1%
5 1361
15.6%
1 1109
12.7%
9 834
 
9.6%
6 471
 
5.4%
3 363
 
4.2%
7 252
 
2.9%
2 198
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 2195
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10921
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2558
23.4%
. 2195
20.1%
4 1580
14.5%
5 1361
12.5%
1 1109
10.2%
9 834
 
7.6%
6 471
 
4.3%
3 363
 
3.3%
7 252
 
2.3%
2 198
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10921
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2558
23.4%
. 2195
20.1%
4 1580
14.5%
5 1361
12.5%
1 1109
10.2%
9 834
 
7.6%
6 471
 
4.3%
3 363
 
3.3%
7 252
 
2.3%
2 198
 
1.8%

MedAgeGroupTotalSpent
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size98.8 KiB
219.0
1141 
522.0
744 
657.5
310 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters10975
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row522.0
2nd row657.5
3rd row522.0
4th row219.0
5th row219.0

Common Values

ValueCountFrequency (%)
219.0 1141
52.0%
522.0 744
33.9%
657.5 310
 
14.1%

Length

2023-11-05T19:51:51.210367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-05T19:51:51.454598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
219.0 1141
52.0%
522.0 744
33.9%
657.5 310
 
14.1%

Most occurring characters

ValueCountFrequency (%)
2 2629
24.0%
. 2195
20.0%
0 1885
17.2%
5 1364
12.4%
1 1141
10.4%
9 1141
10.4%
6 310
 
2.8%
7 310
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8780
80.0%
Other Punctuation 2195
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2629
29.9%
0 1885
21.5%
5 1364
15.5%
1 1141
13.0%
9 1141
13.0%
6 310
 
3.5%
7 310
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 2195
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10975
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2629
24.0%
. 2195
20.0%
0 1885
17.2%
5 1364
12.4%
1 1141
10.4%
9 1141
10.4%
6 310
 
2.8%
7 310
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2629
24.0%
. 2195
20.0%
0 1885
17.2%
5 1364
12.4%
1 1141
10.4%
9 1141
10.4%
6 310
 
2.8%
7 310
 
2.8%

Interactions

2023-11-05T19:51:27.160283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:33.667682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:36.672384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:40.112142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-11-05T19:50:46.522845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-11-05T19:51:19.566286image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:22.764055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:26.200586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:30.676027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:36.076115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:39.356232image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:42.727792image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:45.901126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:49.113050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:53.274397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:59.407525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:03.809401image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:07.186336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:09.956040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:13.386748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:16.301193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:19.765128image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:22.998185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:26.438719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:30.929688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:36.271463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:39.716929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:42.935998image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:46.116638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:49.287684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:53.571168image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:59.674831image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:04.016270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:07.360094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:10.196672image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:13.569968image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:16.489846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:19.941890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:23.192982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:26.654632image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:31.230322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:36.473346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:39.886500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:43.125198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:46.321789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:49.515320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:53.845012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:50:59.912707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:04.223167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:07.533300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:10.442422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:13.729674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:16.725912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:20.160956image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:23.355375image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-05T19:51:26.903033image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-05T19:51:51.760510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
IncomeRecencyWinesFruitsMeatFishSweetGoldNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthTotalSpentAgeCustomerLastDaysEducationKidhomeTeenhomeResponseChildrenFamilySizeTotalAcceptedCmpAcceptedSmthHasChildrenBoughtCatalogAgeGroupInRelationshipEducationLevelMedEducationTotalSpentMedAgeGroupTotalSpent
Income1.0000.0100.8460.5930.8310.5880.5780.524-0.1960.5890.8040.750-0.6440.8650.221-0.0240.1720.4210.3490.2600.3350.2220.2500.3830.5430.6000.1910.0190.1790.1720.191
Recency0.0101.0000.0170.0260.0280.0140.0260.0190.007-0.0000.0290.003-0.0200.0220.0150.0270.0000.0650.0470.2100.0330.0300.0000.0000.0210.0000.0710.0550.0000.0000.071
Wines0.8460.0171.0000.5140.8290.5200.5050.5740.0510.7450.8300.805-0.4020.9350.2380.1530.1160.4110.1120.2660.2200.1560.3020.4730.3540.6310.1380.0340.1430.1160.138
Fruits0.5930.0260.5141.0000.7180.7040.6920.569-0.1190.4730.6370.582-0.4550.6870.0290.1250.0720.3160.1220.1510.2700.1870.1300.1790.4580.4070.0470.0000.0910.0720.047
Meat0.8310.0280.8290.7181.0000.7310.7040.648-0.0450.6940.8520.788-0.5000.9400.1220.1500.0700.3630.2290.2580.3590.2440.1820.2940.6080.5060.0900.0460.0760.0700.090
Fish0.5880.0140.5200.7040.7311.0000.7020.565-0.1310.4660.6600.579-0.4710.6990.0310.1290.0640.3270.1410.1260.2890.2000.1180.1780.4860.4260.0790.0000.0750.0640.079
Sweet0.5780.0260.5050.6920.7040.7021.0000.542-0.1130.4590.6340.582-0.4590.674-0.0030.1150.0730.3190.1110.1340.2690.1830.1100.1680.4530.4190.0460.0410.0950.0730.046
Gold0.5240.0190.5740.5690.6480.5650.5421.0000.0960.5740.6580.541-0.2670.6980.0770.2270.0830.2830.0310.1610.1750.1320.1300.2300.2810.5270.0710.0430.1060.0830.071
NumDealsPurchases-0.1960.0070.051-0.119-0.045-0.131-0.1130.0961.0000.294-0.0560.0920.397-0.0210.0940.2170.0130.2120.3500.0970.3710.2710.0770.1360.5670.2220.0480.0200.0150.0130.048
NumWebPurchases0.589-0.0000.7450.4730.6940.4660.4590.5740.2941.0000.6300.674-0.0980.7370.1660.2040.0800.3170.1380.1960.1620.1090.1330.2510.2340.6380.1010.0000.0790.0800.101
NumCatalogPurchases0.8040.0290.8300.6370.8520.6600.6340.658-0.0560.6301.0000.714-0.5440.8940.1860.1220.0720.4210.1690.2360.3000.2050.2090.3450.4960.6100.1230.0000.0770.0720.123
NumStorePurchases0.7500.0030.8050.5820.7880.5790.5820.5410.0920.6740.7141.000-0.4770.8160.1720.1120.1050.4040.0840.1450.2010.1360.1360.2430.3150.6560.1270.0000.1000.1050.127
NumWebVisitsMonth-0.644-0.020-0.402-0.455-0.500-0.471-0.459-0.2670.397-0.098-0.544-0.4771.000-0.479-0.1360.3100.0640.3410.2380.1300.3290.2210.1050.1440.5670.3500.1060.0720.0540.0640.106
TotalSpent0.8650.0220.9350.6870.9400.6990.6740.698-0.0210.7370.8940.816-0.4791.0000.1650.1800.0920.4400.2250.2950.3270.2240.2670.4230.5370.6730.1340.0000.0980.0920.134
Age0.2210.0150.2380.0290.1220.031-0.0030.0770.0940.1660.1860.172-0.1360.1651.000-0.0100.1110.2350.3360.0670.2130.1560.0570.0760.3140.1970.8760.0840.1460.1110.876
CustomerLastDays-0.0240.0270.1530.1250.1500.1290.1150.2270.2170.2040.1220.1120.3100.180-0.0101.0000.0440.0410.0000.2010.0310.0250.0140.0180.0000.1210.0000.0000.0390.0440.000
Education0.1720.0000.1160.0720.0700.0640.0730.0830.0130.0800.0720.1050.0640.0920.1110.0441.0000.0500.1050.0960.0330.0320.0000.0320.0000.1340.1120.0001.0001.0000.112
Kidhome0.4210.0650.4110.3160.3630.3270.3190.2830.2120.3170.4210.4040.3410.4400.2350.0410.0501.0000.0530.0740.6210.5260.1530.2070.5380.4560.2090.0000.0370.0500.209
Teenhome0.3490.0470.1120.1220.2290.1410.1110.0310.3500.1380.1690.0840.2380.2250.3360.0000.1050.0531.0000.1600.5640.4680.0950.1070.6090.0780.2510.0110.0860.1050.251
Response0.2600.2100.2660.1510.2580.1260.1340.1610.0970.1960.2360.1450.1300.2950.0670.2010.0960.0740.1601.0000.2050.2640.4250.3660.2040.1820.0260.1470.0820.0960.026
Children0.3350.0330.2200.2700.3590.2890.2690.1750.3710.1620.3000.2010.3290.3270.2130.0310.0330.6210.5640.2051.0000.7620.1610.2441.0000.2640.1430.0450.0340.0330.143
FamilySize0.2220.0300.1560.1870.2440.2000.1830.1320.2710.1090.2050.1360.2210.2240.1560.0250.0320.5260.4680.2640.7621.0000.1030.1870.7600.2230.1090.6000.0340.0320.109
TotalAcceptedCmp0.2500.0000.3020.1300.1820.1180.1100.1300.0770.1330.2090.1360.1050.2670.0570.0140.0000.1530.0950.4250.1610.1031.0000.9990.2810.2470.0350.0000.0180.0000.035
AcceptedSmth0.3830.0000.4730.1790.2940.1780.1680.2300.1360.2510.3450.2430.1440.4230.0760.0180.0320.2070.1070.3660.2440.1870.9991.0000.2390.2480.0200.0000.0240.0320.020
HasChildren0.5430.0210.3540.4580.6080.4860.4530.2810.5670.2340.4960.3150.5670.5370.3140.0000.0000.5380.6090.2041.0000.7600.2810.2391.0000.2410.1090.0510.0000.0000.109
BoughtCatalog0.6000.0000.6310.4070.5060.4260.4190.5270.2220.6380.6100.6560.3500.6730.1970.1210.1340.4560.0780.1820.2640.2230.2470.2480.2411.0000.1750.0090.0970.1340.175
AgeGroup0.1910.0710.1380.0470.0900.0790.0460.0710.0480.1010.1230.1270.1060.1340.8760.0000.1120.2090.2510.0260.1430.1090.0350.0200.1090.1751.0000.0000.1130.1121.000
InRelationship0.0190.0550.0340.0000.0460.0000.0410.0430.0200.0000.0000.0000.0720.0000.0840.0000.0000.0000.0110.1470.0450.6000.0000.0000.0510.0090.0001.0000.0000.0000.000
EducationLevel0.1790.0000.1430.0910.0760.0750.0950.1060.0150.0790.0770.1000.0540.0980.1460.0391.0000.0370.0860.0820.0340.0340.0180.0240.0000.0970.1130.0001.0001.0000.113
MedEducationTotalSpent0.1720.0000.1160.0720.0700.0640.0730.0830.0130.0800.0720.1050.0640.0920.1110.0441.0000.0500.1050.0960.0330.0320.0000.0320.0000.1340.1120.0001.0001.0000.112
MedAgeGroupTotalSpent0.1910.0710.1380.0470.0900.0790.0460.0710.0480.1010.1230.1270.1060.1340.8760.0000.1120.2090.2510.0260.1430.1090.0350.0200.1090.1751.0000.0000.1130.1121.000

Missing values

2023-11-05T19:51:31.742472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-05T19:51:32.664521image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EducationIncomeKidhomeTeenhomeRecencyWinesFruitsMeatFishSweetGoldNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthResponseTotalSpentAgeChildrenFamilySizeTotalAcceptedCmpAcceptedSmthHasChildrenBoughtCatalogAgeGroupCustomerLastDaysInRelationshipEducationLevelMedEducationTotalSpentMedAgeGroupTotalSpent
0Graduation58138.000586358854617288883810471161757010001Average6630Graduate415.0522.0
1Graduation46344.0113811162162112502760230011Elderly1130Graduate415.0657.5
2Graduation71613.00026426491271112142182104077649020001Average3121Graduate415.0522.0
3Graduation26646.010261142010352204605330130010Youth1391Graduate415.0219.0
4PhD58293.010941734311846271555365042233130011Youth1611Postgraduate496.0219.0
5Master62513.00116520429804214264106071647130011Average2931Postgraduate390.0522.0
6Graduation55635.001342356516450492747376059043120011Youth5930Graduate415.0219.0
7PhD33454.01032761056312324048016929130010Youth4171Postgraduate496.0219.0
8PhD30351.01019140243321302914640130010Youth3881Postgraduate496.0219.0
11Basic7500.0005961611111161203806138020000Youth5931Undergraduate57.0219.0
EducationIncomeKidhomeTeenhomeRecencyWinesFruitsMeatFishSweetGoldNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthResponseTotalSpentAgeChildrenFamilySizeTotalAcceptedCmpAcceptedSmthHasChildrenBoughtCatalogAgeGroupCustomerLastDaysInRelationshipEducationLevelMedEducationTotalSpentMedAgeGroupTotalSpent
2229Graduation24434.0209328200172212705042240011Youth421Graduate415.0219.0
2230Graduation11012.010822432671233312908430121111Youth4700Graduate415.0219.0
2231Master44802.00071853101431310202941280104944010001Youth6770Postgraduate390.0219.0
2232Graduation26816.000505163431003402228010000Youth6810Graduate415.0219.0
2234Graduation34421.010813376291102703040130010Youth3631Graduate415.0219.0
2235Graduation61223.001467094318242118247293450134147130011Average3811Graduate415.0522.0
2236PhD64014.0215640603000878257044468351111Elderly191Postgraduate496.0657.5
2237Graduation56981.00091908482173212241231360124133011101Youth1550Graduate415.0219.0
2238Master69245.001842830214803061265103084358130011Average1561Postgraduate390.0522.0
2239PhD52869.0114084361212133147117260240011Elderly6221Postgraduate496.0657.5

Duplicate rows

Most frequently occurring

EducationIncomeKidhomeTeenhomeRecencyWinesFruitsMeatFishSweetGoldNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthResponseTotalSpentAgeChildrenFamilySizeTotalAcceptedCmpAcceptedSmthHasChildrenBoughtCatalogAgeGroupCustomerLastDaysInRelationshipEducationLevelMedEducationTotalSpentMedAgeGroupTotalSpent# duplicates
21Graduation18690.00077617234191112806055020001Average5481Graduate415.0522.03
23Graduation18929.000153208234181104608524020000Youth4981Graduate415.0219.03
57Graduation39922.010302912591913623048015631130010Youth5001Graduate415.0219.03
84Graduation67445.00163757802172980115961260117440130011Youth6861Graduate415.0219.03
107Graduation83844.000579013134575311911441110157462021101Elderly4131Graduate415.0657.53
133Master63841.001646351510020713119396090846120011Average4340Postgraduate390.0522.03
0Basic20425.01054125316172203705728130010Youth6081Undergraduate57.0219.02
1Basic22634.000472231186461212809648020001Average5291Undergraduate57.0522.02
2Basic24594.0109413610091103502935130010Youth2011Undergraduate57.0219.02
3Basic24882.010521410290361112608036131111Youth6581Undergraduate57.0219.02